Bibliographic record
Abstract
Invasive fungal infections (IFI) such as mucormycosis are causing devastating morbidity and mortality in immunocompromised patients as anti-fungal agents do not work in the setting of suppressed immune system. COVID-19 pandemic has created a novel landscape for IFI in post-pandemic patients, which results from severe immune suppression caused by COVID-19 infection, comorbidities (diabetes, obesity) and immunosuppressive treatments such as steroids. The antigen-antibody interaction has been employed in radioimmunotherapy (RIT) to deliver lethal doses of ionizing radiation emitted by radionuclides to targeted cells and has demonstrated efficacy in several cancers. One of the advantages of RIT is its independence of immune status of a host which is crucial for immunosuppressed post-COVID-19 patients. In present work we targeted with RIT fungal pan-antigens 1,3-beta-glucan and melanin pigment, which are present in the majority of pathogenic fungi, thus making such targeting pathogen-agnostic. We demonstrated in experimental murine mucormycosis that 177Lutetium-labeled antibodies to these two antigens effectively decreased the fungal burden in major organs including the brain. These results are encouraging because they show the effectiveness of pathogen-agnostic RIT in significantly decreasing fungal burden in vivo, while they can be also potentially applied to treat the broad range of invasive fungal infections which express pan-antigens 1,3-beta-glucan or melanin.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.702 | 0.464 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".